
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
import os
import matplotlib.pyplot as plt
from keras import utils
import sys
import cv2
import numpy as np
plt.switch_backend('agg')

os.environ["CUDA_VISIBLE_DEVICES"] = "3"

W=64
H=64
C=3
dh=8
dw=8
img_root=r"/data/soft/javad/COCO/faces"
# img_root = r"/data/soft/javad/threeone/fin"
BATCH_SIZE=32
def mkdirs(dir):
    if not os.path.exists(dir):
        os.mkdir(dir)

mkdirs("./preview")

def load_data():
    path=r"/data/soft/javad/COCO/mnist.npz"
    with np.load(path, allow_pickle=True) as f:
        x_train, y_train = f['x_train'], f['y_train']
        x_test, y_test = f['x_test'], f['y_test']
    return (x_train, y_train), (x_test, y_test)


def read_image(img):
    im = cv2.imread(img)
    # im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB)
    im = cv2.resize(im, (W,H),interpolation=cv2.INTER_CUBIC)
    im = (im.astype(np.float32) - 127.5) / 127.5
    return im


class Dse(utils.Sequence):
    def __init__(self, images_root, shuffle=True,train=True):
        self.images_root = images_root
        self.imgs = self.init()
        self.shuffle = shuffle
        self.train=train

    def init(self):
        imgs = []
        # labels = []
        images = os.listdir(self.images_root)
        for i, dir in enumerate(images):
            imgs.append(os.path.join(self.images_root, dir))
            # labels.append(i)
        return imgs

    def __len__(self):
        return len(self.imgs) // BATCH_SIZE

    def __getitem__(self, item):
        images = self.imgs[item * BATCH_SIZE:(item + 1) * BATCH_SIZE]
        # labels = self.labels[item*BATCH_SIZE:(item+1)*BATCH_SIZE]
        images = [read_image(image_file) for image_file in images]

        return np.array(images, np.float)


class DCGAN():
    def __init__(self):
        # Input shape
        self.img_rows = H
        self.img_cols = W
        self.channels = C
        self.img_shape = (self.img_rows, self.img_cols, self.channels)
        self.latent_dim = 100

        optimizer = Adam(0.0002, 0.5)

        # Build and compile the discriminator
        self.discriminator = self.build_discriminator()
        self.discriminator.compile(loss='binary_crossentropy',
            optimizer=optimizer,
            metrics=['accuracy'])

        # Build the generator
        self.generator = self.build_generator()

        # The generator takes noise as input and generates imgs
        z = Input(shape=(self.latent_dim,))
        img = self.generator(z)

        # For the combined model we will only train the generator
        self.discriminator.trainable = False

        # The discriminator takes generated images as input and determines validity
        valid = self.discriminator(img)

        # The combined model  (stacked generator and discriminator)
        # Trains the generator to fool the discriminator
        self.combined = Model(z, valid)
        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)

    def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * dh * dw, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((dh, dw, 128)))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(UpSampling2D())
        model.add(Conv2D(32, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img)

    def build_discriminator(self):

        model = Sequential()

        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
        model.add(ZeroPadding2D(padding=((0,1),(0,1))))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Flatten())
        model.add(Dense(1, activation='sigmoid'))

        model.summary()

        img = Input(shape=self.img_shape)
        validity = model(img)

        return Model(img, validity)

    def train(self, epochs, batch_size=128, save_interval=50):

        # Load the dataset
        traingen = Dse(img_root)

        # Rescale -1 to 1
        # X_train = X_train / 127.5 - 1.
        # X_train = np.expand_dims(X_train, axis=3)

        # Adversarial ground truths
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))

        for epoch in range(epochs):

            # ---------------------
            #  Train Discriminator
            # ---------------------

            # Select a random half of images
            for idx,imgs in enumerate(traingen):
                # idx = np.random.randint(0, X_train.shape[0], batch_size)
                # imgs = X_train[idx]

                # Sample noise and generate a batch of new images
                noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
                gen_imgs = self.generator.predict(noise)

                # Train the discriminator (real classified as ones and generated as zeros)
                d_loss_real = self.discriminator.train_on_batch(imgs, valid)
                d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
                d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

                # ---------------------
                #  Train Generator
                # ---------------------

                # Train the generator (wants discriminator to mistake images as real)
                g_loss = self.combined.train_on_batch(noise, valid)

                # Plot the progress
                print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

                # If at save interval => save generated image samples
                if epoch % save_interval and idx % 500 == 0:
                    # self.save_imgs(epoch,idx)
                    self.preview_images(epoch,idx)

    def preview_images(self,epoch, idx):
        r, c = 5, 5
        noise = np.random.normal(0, 1, (r * c, self.latent_dim))
        gen_imgs = self.generator.predict(noise)

        gen_imgs = 0.5 * gen_imgs + 0.5
        gen_imgs = (gen_imgs * 255.).astype(np.uint8)
        for i in range(r):
            image = gen_imgs[i]
            cv2.imwrite("preview/comic_%d_%d.png" % (epoch,idx),image)

    def save_imgs(self, epoch,idx):
        r, c = 5, 5
        noise = np.random.normal(0, 1, (r * c, self.latent_dim))
        gen_imgs = self.generator.predict(noise)

        # Rescale images 0 - 1
        gen_imgs = 0.5 * gen_imgs + 0.5

        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i,j].imshow(gen_imgs[cnt, :,:,:])
                axs[i,j].axis('off')
                cnt += 1
        fig.savefig("images/mnist_%d_%d.png" % (epoch,idx))
        plt.close()


if __name__ == '__main__':
    dcgan = DCGAN()
    dcgan.train(epochs=80, batch_size=BATCH_SIZE, save_interval=10)
